What Data Looks Like for This Category
Core data for this category comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES furnace batch records, mold management systems, customer drawings, and technical specifications. Daily stored documents include foundry process cards, batching lists, furnace batch records, mold history logs, first article inspection records, material test reports, and nonconforming product review forms.
Data updates trigger per furnace batch, per production batch, or daily. Fields include alloy composition ratios, pouring temperatures, mold usage counts, furnace batch numbers, defect types, and more. Units include percentage, degrees Celsius, millimeters, and others. Each individual document links to multi-dimensional production parameters and quality records.
Constraints on Vector Models and Indexing
Multi-dimensional linked production and quality data requires indexes to support cross-document associated recall, rather than only retrieving single fragments.
Frequently updated furnace batch and batch data requires indexes to support incremental updates, reducing resource consumption from full index rebuilding.
Professional terms such as alloy batching ratios and defect types in industrial scenarios require vector models to accurately understand industrial semantics.
The structure where each individual document links to multiple parameters requires that chunking preserves field association relationships, avoiding context loss after splitting.
The business requirement to trace defects back to furnace batches and mold usage counts requires indexes to support precise filtered recall based on fields such as furnace batch number and mold times.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Covers core fields of a single furnace batch record or process card, preserves semantic completeness |
| Overlap Length | 100–150 characters | Preserves key identifiers such as furnace batch number and mold times across chunks, avoids context breaks |
| Similarity Threshold | 0.72–0.85 | Filters low-correlation industrial parameter entries, improves recall accuracy |
| Single Search Limit | Top 8 entries | Adapts to the context capacity of single-round question answering, avoids excessive redundant data |
| Maximum Context | 4000–6000 maxToken | Accommodates content from multiple linked documents, meets context requirements for defect tracing |
| Default Token Count | 1024 defaultToken | Adapts to tokenization length of basic industrial documents, optimizes basic retrieval performance |
| Incremental Update Toggle | Enabled | Adapts to the high-frequency data update rhythm of per furnace batch and per batch, reduces full rebuild time |
Three Common Configuration Errors
- Phenomenon: Search results only return a single furnace batch record, and cannot link to the corresponding mold history. Cause: Associated cross-document fields were not preserved during chunking, and the binding relationship between furnace batch number and mold times was lost after splitting.
- Phenomenon: Vector index updates take too long, triggering task timeouts. Cause: Incremental update mode was not enabled, and full index rebuild was executed for each update, which does not match the high-frequency furnace batch update rhythm.
- Phenomenon: External open-source vector index model fails to load, and the interface displays a model loading failure prompt. Cause: The API address and secret key were not filled in according to the FastGPT v4.17.0 external model configuration process, and the correct model path was not specified when adapting to free open-source models.
How to Verify Proper Configuration
- Upload a single furnace batch record and mold history log, check if chunked documents retain key fields such as furnace batch number and mold times.
- Initiate a defect tracing test query, verify that search results include linked furnace batch, mold, and process card data.
- Trigger an incremental update task, check if index update time meets business rhythm requirements.
- Adjust the similarity threshold, verify that search result relevance meets expectations.
The values provided are common starting points and should be measured against the reader's own samples.